Lifted MEU by weighted model counting

Udi Apsel, Ronen I. Brafman

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים

תקציר

Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) engines for exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, such as influence diagrams, and Markov decision networks (MDN). In this paper we show how this technique can be applied to such models. First, we show how WMC can be used to solve (propositional) MDNs. Then, we show how this can be extended to handle a first-order model - the Markov Logic Decision Network (MLDN). WMC offers two central benefits: it is a very simple and very efficient technique. This is particularly true for the first-order case, where the WMC approach is simpler conceptually, and, in many cases, more effective computationally than the existing methods for solving MLDNs via first-order variable elimination, or via propositionalization. We demonstrate the above empirically.

שפה מקוריתאנגלית אמריקאית
כותר פרסום המארחAAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
עמודים1861-1867
מספר עמודים7
סטטוס פרסוםפורסם - 7 נוב׳ 2012
אירוע26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON, קנדה
משך הזמן: 22 יולי 201226 יולי 2012

סדרות פרסומים

שםProceedings of the National Conference on Artificial Intelligence
כרך3

כנס

כנס26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
מדינה/אזורקנדה
עירToronto, ON
תקופה22/07/1226/07/12

ASJC Scopus subject areas

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